Novel Strategy for Mining and Identification of Acylcarnitines Using Data-Independent-Acquisition-Based Retention Time Prediction Modeling and Pseudo-Characteristic Fragmentation Ion Matching.

It is a challenging work to screen, identify, and quantify acylcarnitines in complex biological samples. A method, based on the retention time (RT) prediction and data-independent acquisition strategies, was proposed for the large-scale identification of acylcarnitines using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS). Relative cumulative eluotropic strength was introduced as a novel descriptor in building a linear prediction model, which not only solves the problem that acylcarnitines with long carbon chains cannot be well predicted in traditional models but also proves its robustness and transferability across instruments in two data sets that were acquired in distinct chromatography conditions. The accessibility of both predictive RT and MS spectra of suspect features effectively reduced about 30% false-positive results, and consequently, 150 and 186 acylcarnitines were identified in the rat liver and human plasma (NIST SRM 1950), respectively. This method provides a new approach in large-scale analysis of acylcarnitine in lipidomic studies and can also be extended to the analysis of other lipids.